Special Issue Information

Dear Colleagues,

Hybrid-electric vehicles (HEVs) constitute an interesting solution for the reduction of fuel consumption and pollutant emissions with respect to conventional vehicles, while maintaining comparable performances and ranges. They can also be realized with limited modifications to existing manufacturing processes; moreover, they can normally operate not only in hybrid mode, but also in purely electric mode, this way reaching the condition of zero emissions.

The basis of the growth of hybrid technology is electric storage, and electrochemical batteries, and even supercapacitors, are researched and developed in a variety of chemistries and configurations to better meet the continuously changing technical and economical requirements for battery-powered electric vehicles (EVs), hybrid electric vehicles (HEVs), but, to actually achieve the potential advantages of HEVs the powertrain structure and the energy management logic have to be carefully selected and matched.

The special issue covers current trends and future developments of HEV technology, including both mechanical as well as electrical engineering aspects.

Abstract: Lifetime testing of batteries for hybrid-electrical vehicles (HEV) is usually performed in the lab, either at the cell, module or battery pack level. Complementary field tests of battery packs in vehicles are also often performed. There are, however, difficulties related to field testing of battery-packs. Some examples are cost issues and the complexity of continuously collecting battery performance data, such as capacity fade and impedance increase. In this paper, a novel field test equipment designed primarily for lithium-ion battery cell testing is presented. This equipment is intended to be used on conventional vehicles, not hybrid vehicles, as a cheaper and faster field testing method for batteries, compared to full scale HEV testing. The equipment emulates an HEV environment for the tested battery cell by using real time vehicle sensor information and the existing starter battery as load and source. In addition to the emulated battery cycling, periodical capacity and pulse testing capability are implemented as well. This paper begins with presenting some background information about hybrid electrical vehicles and describing the limitations with today’s HEV battery testing. Furthermore, the functionality of the test equipment is described in detail and, finally, results from verification of the equipment are presented and discussed.

Abstract: Emerging green-energy transportation, such as hybrid electric vehicles (HEVs)
and plug-in HEVs (PHEVs), has a great potential for reduction of fuel consumption and
greenhouse emissions. The lithium-ion battery system used in these vehicles, however,
is bulky, expensive and unreliable, and has been the primary roadblock for transportation
electrification. Meanwhile, few studies have considered user-specific driving behavior
and its significant impact on (P)HEV fuel efficiency, battery system lifetime, and the
environment. This paper presents a detailed investigation of battery system modeling and
real-world user-specific driving behavior analysis for emerging electric-drive vehicles. The
proposed model is fast to compute and accurate for analyzing battery system run-time
and long-term cycle life with a focus on temperature dependent battery system capacity
fading and variation. The proposed solution is validated against physical measurement using
real-world user driving studies, and has been adopted to facilitate battery system design and
optimization. Using the collected real-world hybrid vehicle and run-time driving data, we
have also conducted detailed analytical studies of users’ specific driving patterns and their
impacts on hybrid vehicle electric energy and fuel efficiency. This work provides a solid
foundation for future energy control with emerging electric-drive applications.

Abstract: Compared to conventional vehicles Hybrid Electric Vehicles (HEVs) provide fairly high fuel economy with lower emissions. To enhance HEV performance in terms of fuel economy and emissions, and ensure user satisfaction with driving performance, the need for simultaneous optimization for the main parameters of powertrain components and control system is inevitable. However, this problem is challenging due to the large amount of coupling design parameters, conflicting design objectives and nonlinear constraints. Considering the defect of the methods which convert multi-objective optimization problems into single-objective ones, a comprehensive methodology based on the non-dominated sorting genetic algorithms II (NSGA II) to achieve parameter optimization for powertrain components and control system simultaneously and successfully find the Pareto-optimal solutions set is presented in this paper. A case simulation is carried out and simulated by ADVISOR, The simulation results show that this method can produce many Pareto-optimal solutions and a satisfactory solution can be selected by decision-makers according to their requirements. The results demonstrate the effectiveness of the algorithms proposed in this paper.

Abstract: Fuel economy improvement on medium-duty tactical truck has and continues to be a significant initiative for the U.S. Army. The focus of this study is the investigation of Automated Manual Transmissions (AMT) and mild hybridization powertrain that have potential to improve the fuel economy of the 2.5-ton cargo trucks. The current platform uses a seven-speed automatic transmission. This study utilized a combination of on-road experimental vehicle data and analytical vehicle modeling and simulation. This paper presents the results of (1) establishment of a validated, high fidelity baseline analytical vehicle model, (2) modeling and simulation of two AMTs and their control strategy, (3) optimization of transmissions shift schedules, and (4) modeling and simulation of engine idle stop/start and Belt-Integrated-Starter-Generator (B-ISG) systems to improve the fuel economy. The fuel economy discrepancy between experimental average and the baseline simulation result was 2.87%. The simulation results indicated a 14.5% and 12.2% fuel economy improvement for the 10-speed and 12-speed AMT respectively. A stop/start system followed by a B-ISG mild hybrid system incorporating regenerative braking was estimated to improve fuel economy 3.39% and 10.2% respectively.

Abstract: Hybrid power systems, formed by combining high-energy-density batteries and high-power-density ultracapacitors in appropriate ways, provide high-performance and high-efficiency power systems for electric vehicle applications. This paper first establishes dynamic models for the ultracapacitor, the battery and a passive hybrid power system, and then based on the dynamic models a comparative simulation between a battery only power system and the proposed hybrid power system was done under the UDDS (Urban Dynamometer Driving Schedule). The simulation results showed that the hybrid power system could greatly optimize and improve the efficiency of the batteries and their dynamic current was also decreased due to the participation of the ultracapacitors, which would have a good influence on batteries’ cycle life. Finally, the parameter matching for the passive hybrid power system was studied by simulation and comparisons.

Abstract: Based on the traditional regenerative braking electrical circuit, a novel energy recovery system for the main and auxiliary sources of electric vehicles (EVs) has been developed to improve their energy efficiency. The electrical circuit topology is presented in detail. During regenerative braking, the recovered mechanical energy is stored in both the main source and the auxiliary source at the same time. The mathematical model of the proposed system is derived step by step. Combining the merits and defects of H2 optimal control and H∞ robust control, a H2/H∞controller is designed to guarantee both the system performance and robust stability. The perfect match between the simulated and experimental results validates the notion that the proposed novel energy recovery system is both feasible and effective, as more energy is recovered than that with the traditional energy recovery systems, in which recovered energy is stored only in the main source.

Abstract: State of Charge (SoC) estimation is one of the most significant and difficult techniques to promote the commercialization of electric vehicles (EVs). Suffering from various interference in vehicle driving environment and model uncertainties due to the strong time-variant property and inconsistency of batteries, the existing typical SoC estimators such as coulomb counting and extended Kalman filter cannot perform their theoretically optimal efficacy in practical applications. Aiming at enhancing the robustness of SoC estimation and improving accuracy under the real driving conditions with noises and uncertainties, this paper proposes a framework consisting of (1) an adaptive-κ nonlinear diffusion filter to reduce the noise in current measurement, (2) a self-learning strategy to estimate and remove the zero-drift, (3) a coulomb counting algorithm to realize open-loop SoC estimation, (4) an H∞ filter to implement closed-loop robust estimation, and (5) a data fusion unite to achieve the final estimation by integrating the advantages of the two SoC estimators. The availability and efficacy of each component have been demonstrated based on comparative studiesin simulation with the conventional approaches respectively, under the testing conditions of noises with various signal-noise-ratios, varying zero-drifts, and different model errors. The overall framework has also been verified to rationally and efficiently combine these components and achieve robust estimation results in the presence of kinds of noises and uncertainties.

Abstract: In order to safely and efficiently use the power as well as to extend the lifetime of the traction battery pack, accurate estimation of State of Charge (SoC) is very important and necessary. This paper presents an adaptive observer-based technique for estimating SoC of a lithium-ion battery pack used in an electric vehicle (EV). The RC equivalent circuit model in ADVISOR is applied to simulate the lithium-ion battery pack. The parameters of the battery model as a function of SoC, are identified and optimized using the numerically nonlinear least squares algorithm, based on an experimental data set. By means of the optimized model, an adaptive Luenberger observer is built to estimate online the SoC of the lithium-ion battery pack. The observer gain is adaptively adjusted using a stochastic gradient approach so as to reduce the error between the estimated battery output voltage and the filtered battery terminal voltage measurement. Validation results show that the proposed technique can accurately estimate SoC of the lithium-ion battery pack without a heavy computational load.